{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "import numpy as np\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "\r\n",
    "from sklearn import datasets\r\n",
    "from sklearn.svm import LinearSVC\r\n",
    "from sklearn.pipeline import Pipeline\r\n",
    "from sklearn.preprocessing import StandardScaler\r\n",
    "\r\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "#iris = datasets.load_iris()\r\n",
    "\r\n",
    "#iris.data, iris.target"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "iris = datasets.load_iris()\r\n",
    "x=iris['data'][:,(2,3)]\r\n",
    "y=(iris['target']==2).astype(np.float64)\r\n",
    "# label_filter = (y == 0) | (y == 1)\r\n",
    "# x=x[label_filter]\r\n",
    "# y=y[label_filter]\r\n",
    "#svm_clf = SVC(kernel='linear',C=float('inf'))\r\n",
    "svm_clf= Pipeline((\r\n",
    "    ('std',StandardScaler()),\r\n",
    "    ('linear_svc',LinearSVC(C=1)),\r\n",
    "))\r\n",
    "svm_clf.fit(x,y)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Pipeline(steps=[('std', StandardScaler()), ('linear_svc', LinearSVC(C=1))])"
      ],
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;std&#x27;, StandardScaler()), (&#x27;linear_svc&#x27;, LinearSVC(C=1))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;std&#x27;, StandardScaler()), (&#x27;linear_svc&#x27;, LinearSVC(C=1))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearSVC</label><div class=\"sk-toggleable__content\"><pre>LinearSVC(C=1)</pre></div></div></div></div></div></div></div>"
      ]
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "svm_clf.predict([[5.5,1.7],[5.5,1.8]])"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([1., 1.])"
      ]
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "scaler = StandardScaler()\r\n",
    "\r\n",
    "svm_clf1 = LinearSVC(C=1,random_state=42)\r\n",
    "svm_clf2 = LinearSVC(C=100,random_state=42)\r\n",
    "\r\n",
    "scaled_svm_clf1 = Pipeline((\r\n",
    "    ('std',scaler),\r\n",
    "    ('linear_svc',svm_clf1)\r\n",
    "))\r\n",
    "scaled_svm_clf2 = Pipeline((\r\n",
    "    ('std',scaler),\r\n",
    "    ('linear_svc',svm_clf2)\r\n",
    "))\r\n",
    "\r\n",
    "scaled_svm_clf1.fit(x,y)\r\n",
    "scaled_svm_clf2.fit(x,y)\r\n",
    "\r\n",
    "b1 = svm_clf1.decision_function([-scaler.mean_/ scaler.scale_])\r\n",
    "b2 = svm_clf2.decision_function([-scaler.mean_/ scaler.scale_])\r\n",
    "\r\n",
    "w1 = svm_clf1.coef_[0] / scaler.scale_\r\n",
    "w2 = svm_clf2.coef_[0] / scaler.scale_\r\n",
    "\r\n",
    "svm_clf1.intercept_ = np.array([b1])\r\n",
    "svm_clf2.intercept_ = np.array([b2])\r\n",
    "svm_clf1.coef_ = np.array([w1])\r\n",
    "svm_clf2.coef_ = np.array([w2])"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "D:\\python3.8\\lib\\site-packages\\sklearn\\svm\\_base.py:1225: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "def plot_svm_bd(svm_clf,start,end,sv=True):\r\n",
    "    w = svm_clf.coef_[0]\r\n",
    "    b = svm_clf.intercept_[0]\r\n",
    "\r\n",
    "    x0=np.linspace(start,end,200)\r\n",
    "    decision_bd = w[0]/w[1] * x0 - b/w[1]\r\n",
    "    margin = 1/w[1]\r\n",
    "    up_bd = decision_bd + margin\r\n",
    "    down_bd = decision_bd - margin\r\n",
    "\r\n",
    "    plt.plot(x0,decision_bd,'k-',linewidth=2)\r\n",
    "    plt.plot(x0,up_bd,'k--',linewidth=2)\r\n",
    "    plt.plot(x0,down_bd,'k--',linewidth=2)\r\n",
    "    \r\n",
    "    if sv:\r\n",
    "        svs = svm_clf.support_vectors_\r\n",
    "        plt.scatter(svs[:,0],svs[:,1],s=180,facecolor='#CAA')\r\n",
    "# plot_svm_bd(svm_clf,0,5.5)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "plt.figure(figsize=(14,4.2))\r\n",
    "\r\n",
    "\r\n",
    "# x0 = np.linspace(0, 5.5, 200)\r\n",
    "# pred_1 = x0 * 5 - 20\r\n",
    "# pred_2 = x0 - 1.8\r\n",
    "# pred_3 = 0.1 * x0 + 0.5\r\n",
    "plt.subplot(121)\r\n",
    "plt.plot(x[:,0][y==1],x[:,1][y==1],\"g^\")\r\n",
    "plt.plot(x[:,0][y==0],x[:,1][y==0],\"bs\")\r\n",
    "plot_svm_bd(svm_clf1, 4, 6,False)\r\n",
    "plt.axis([4, 6, 0.8, 2.8])\r\n",
    "\r\n",
    "\r\n",
    "plt.subplot(122)\r\n",
    "plt.plot(x[:,0][y==1],x[:,1][y==1],\"g^\")\r\n",
    "plt.plot(x[:,0][y==0],x[:,1][y==0],\"bs\")\r\n",
    "plot_svm_bd(svm_clf2, 4, 6,False)\r\n",
    "plt.axis([4, 6, 0.8, 2.8])"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(4.0, 6.0, 0.8, 2.8)"
      ]
     },
     "metadata": {},
     "execution_count": 7
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1008x302.4 with 2 Axes>"
      ],
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5nW1C0vUd1t0s6eYNpQMAYB3UKQBAP3q6yhwAAAAAjBMaIgAAAAClRUME5Ky2VNPkrsm+Lxs6X5vXaZ86TQ8cfiCnZBuX1z4BAIpHnQJWoiECcja1e0rTB6b7vmzoNbdfo6d++5Teces7ckq2cXntEwCgeNQpYCUaIiBHzZvMHYtjfd1cbr42r72LeyVJexf3Fjr7ltc+AQCKR50CTkRDBOSo9SZz/dxc7prbr1nxfZGzb3ntEwCgeNQp4EQ0REBOmjNUzZvM1ZfrG5qpap11aypq9i2vfQIAFI86BbRHQwTkpHWGqmkjM1WrZ92aiph9y2ufAADFo04B7dEQATmZXZg9PkPVVF+ua2ZhpqdxHn3y0Z6WD1Je+wQAKB51CmjPjZt3p6VSqUS1Wi06BgCUmu25iKgUnSNF1CkAKF5edYozRAAAAABKi4YIAAAAQGnREAEAAAAoLRoiAAAAAKVFQ4SRNjEh2Sc+JiZ6H2u+Nq/TPnVaoXfbblVbqmly1yT3Umgjr99VSsc4pSxSI4+26GVF5wBGHXWqnKhTg5dnnaIhwkg7fLi35Wu55vZr9NRvnyr0btutpnZPafrANPdSaCOv31VKxzilLFIjj07SqUXnAEYddaqcqFODl2edoiECtPKu20XdbbtV887bx+IYd9xeJa/fVUrHOKUsrXkApIM6NTqoU8PLkxcaIkAn3nW76Nm31jtvc8ftlfL6XaV0jFPKsjoPgDRQp0YHdWq4efJAQ4TSa53JaSpy9q0569G883Z9uZ7EbEwK8vpdpXSMU8rSLg+A4lGnRgd1avh58rBuQ2T7ZtuP236ow/qP2J7PHg/ZXrZ9RrbuMdsPZuu4pTeStHomp6mo2bd2sx4pzMakIK/fVUrHOKUsnfKkjjqFcUedGh3UqWLy9KubM0S7JF3RaWVEfDoiXhkRr5T0p5L+ISKOtGxyWba+0ldSoJ1TO8xOdFrexqNPPtrT8kGbXZg9YdajvlzXzMJMIXlSktfvKqVjnFKWTnlGwC5Rp5Aq6lSpUKeKydMvR8T6G9nbJX0jIv7FOtt9WdJ3I+K/Z98/JqkSEb/oJVSlUolqlYk6ACiS7blRaRKoUwBQPnnVqdw+Q2T7+WrM0N3asjgk3WV7zvaOdZ6/w3bVdnVxcTGvWAAASKJOAQDay/OiCm+W9P1Vb0O4JCJeJelKSdfbfn2nJ0fETRFRiYjK1q1bc4wFAIAk6hQAoI08G6KrJd3SuiAiDmb/Pi7pdkkX5/jzAADoBXUKAHCCXBoi2y+UNCnp6y3LTrH9gubXki6X1PYKQBie2lJNk7smuTTmAExMSPaJj4mJopMBoE6NDurU4FCngPa6uez2LZJmJb3M9oLt99p+n+33tWz2Vkl3RcQvW5ZtkzRt+35JP5D09xHxrTzDo3dTu6c0fWCaS2MOwOHDvS0HkA/q1HihTg0OdQpor6urzA0bV+8ZjNpSTefecK5+c/Q3Onnzydr/wf2aOJVpobzYndcl+J8ZsK5RusrcsFGnBoM6NVjUKYyb5K4yh/S13siKG6gBAFJDnQJQBBqikqgt1bRzfufxG1nVl+vaOb+T92gDAJJAnQJQFBqikmiddWti9g0AkArqFICi0BCVxOzC7PFZt6b6cl0zCzMFJRo/27b1thwA8Czq1OBRp4D2NhcdAMOx57o9RUcYe4d4VwcAbBh1avCoU0B7nCECAAAAUFo0RAAAAABKi4YIAAAAQGnREJXExETjhmyrHxMF3e8utTwp4diUU22ppsldk1xiGKWV2t++1PKkhGNTTuNcp2iISuLw4d6WD1pqeVLCsSmnqd1Tmj4wzSWGUVqp/e1LLU9KODblNM51ioYIAArWvCHlsTjGjSgBAMkZ9zpFQwQABWu9ISU3ogQApGbc6xQNEQAUqDnr1rwhZX25PpazbwCA0VSGOkVDBAAFap11axrH2TcAwGgqQ52iISqJbdt6Wz5oqeVJCcemXGYXZo/PujXVl+uaWZgpKBFQjNT+9qWWJyUcm3IpQ53aXHQADMehxM5qppYnJRybctlz3Z6iIwBJSO1vX2p5UsKxKZcy1CnOEAEAAAAoLRoiAAAAAKVFQwQAAACgtGiIAAAAAJTWug2R7ZttP277oQ7rL7X9lO357PHxlnVX2P6x7X22P5pn8EGamJDsEx8TE0Un27h2+9N89CKvY5PSMd60qX2WTZs2Nl5tqabJXZN9X58/r3GAMihbrUrpb2heqFOdUaeAwermDNEuSVess833IuKV2eOTkmR7k6S/knSlpPMlvd32+f2EHZbDh3tbXiZ5HZuUjvGxY70tX8/U7ilNH5ju+/r8eY0DlMQulahWpfQ3NDXUqfVRp4CV1m2IImK3pCMbGPtiSfsiYn9E1CV9RdJVGxgHGBnNuzkfi2N93cU5r3GAsqBWAd2hTgEnyuszRK+1fb/tO22/PFt2pqSft2yzkC1ry/YO21Xb1cXFxZxiAcPVejfnfu7inNc4AFboq1ZRpzAOqFPAifJoiO6T9JKIuEDSX0r62kYGiYibIqISEZWtW7fmEAsYruZsWfNuzvXl+oZmzfIaB8AKfdcq6hRGHXUKaK/vhigino6IZ7KvvynpJNtbJB2UdHbLpmdly4Cx1Dpb1rSRWbO8xgHwLGoVQJ0COum7IbI9YTeuAWP74mzMJyT9UNJ5tl9q+7mSrpZ0R78/bxi2betteZnkdWxSOsbP6fBfQaflncwuzB6fLWuqL9c1szBTyDgAnjVutSqlv6GpoU51Rp0C2tu83ga2b5F0qaQtthckfULSSZIUETdKepuk99s+KunXkq6OiJB01PYHJH1b0iZJN0fE3oHsRc4OjeEZ34h8xsnr2KR0jJeX8xlnz3V7khoHKJOy1aqU/obmhTrVGXUKGCxHXn+BclSpVKJarRYdAwBKzfZcRFSKzpEi6hQAFC+vOpXXVeYAAAAAYOTQEAEAAAAoLRqiEbBpk2Sf+Ni0afhZJibaZ5mY6G2cvPYprzwop9pSTZO7JrlULNAn6tTg86CcqFPDQUM0Ao4d6235IB0+3NvyTvLap7zyoJymdk9p+sA0l4oF+kSdGnwelBN1ajhoiACUUvPGgsfiGDcUBAAkhzo1PDREAEqp9caC3FAQAJAa6tTw0BABKJ3mrFvzxoL15TqzbwCAZFCnhouGCEDptM66NTH7BgBIBXVquGiIRsBzOvyWOi0fpG3belveSV77lFcelMvswuzxWbem+nJdMwszBSUCRht1avB5UC7UqeHaXHQArG95uegEzzqU05navPYprzwolz3X7Sk6AjBWqFOdUaewEdSp4eIMEQAAAIDSoiECAAAAUFo0RAAAAABKi4YIAAAAQGkl2RDNzUn2s49ubdq08nnNx6ZNvf381MaZmGg/zsTEcMfIcxwAGGXUqZWoUwBGmSOi6AwnsCshVY9/323EtYpSL7s5juOklAXAaLA9FxGVonOkiDqV/zgpZQEwGvKqU0meIQIAAACAYaAhAgAAAFBaNEQAAAAASmvdhsj2zbYft/1Qh/XvtP2A7Qdtz9i+oGXdY9nyedvVds8HAKAf1CkAQD+6OUO0S9IVa6z/maTJiPiXkqYk3bRq/WUR8cphfDD3OR32ptPyURln27belg9qjDzHAYAc7RJ1qtBxqFMARtnm9TaIiN22t6+xfqbl23skndVvqIsukqobmKdbXu73J6c5zqFDaYyR5zgAkBfqVPHjUKcAjLK8P0P0Xkl3tnwfku6yPWd7x1pPtL3DdtV2dXFxMedYAABIok4BAFZZ9wxRt2xfpkahuaRl8SURcdD2iyTdbftHEbG73fMj4iZlb2OoVCrcKQAAkCvqFACgnVzOENl+haTPS7oqIp5oLo+Ig9m/j0u6XdLFefw8AAB6QZ0CAHTSd0Nk+xxJt0l6V0T8pGX5KbZf0Pxa0uWS2l4BaLW5ucadpm1pYqLfhL2bmHj257c+isiSYp48jOM+AUgTdap8efIwjvsEoD1HrH3W3/Ytki6VtEXSYUmfkHSSJEXEjbY/L+kPJP1j9pSjEVGxfa4as21S4615X46IP+8qlCshPftp1XUi5s7uvG7YWaT08uRhHPcJGDe254Zx5bV+UadWok7lYxz3CRg3edWpdRuiIlBoVkotTx7GcZ+AcTMqDVERqFMrpZYnD+O4T8C4yatO5X2VOQAAAAAYGTREAAAAAEqLhggAAABAaSXfEG3bls7PLCLLWj+3qDx5GMd9AlBO1Kn08uRhHPcJQHu53Zg1TxddJFWr6283KIcOFfez20ktTx7GcZ8AlAd1aqXU8uRhHPcJQHvJnyECAAAAgEGhIQIAAABQWjREAAAAAEqLhggAAABAadEQAQAAACgtGiIAAAAApUVDBAAAAKC0aIgAAAAAlBYNEQAAAIDSoiECAAAAUFo0RAAAAABKi4YIAAAAQGnREAEAAAAoLRoiAAAAAKVFQwQAAACgtLpqiGzfbPtx2w91WG/bN9jeZ/sB269qWXet7Z9mj2u7+Xlzc5LdeExMdLcjUmPb5vNaH72MAQAYPdQpAMBGdXuGaJekK9ZYf6Wk87LHDkl/I0m2z5D0CUmvlnSxpE/YPr2XgIcP979tL2MAAEbSLlGnAAAb0FVDFBG7JR1ZY5OrJH0xGu6RdJrtF0t6o6S7I+JIRDwp6W6tXbAAAOgZdQoAsFF5fYboTEk/b/l+IVvWafkJbO+wXbVdzSkTAABN1CkAQFvJXFQhIm6KiEpEVIrOAgDAatQpABhPeTVEByWd3fL9WdmyTssBABgm6hQAoK28GqI7JP1hdhWf10h6KiJqkr4t6XLbp2cfUr08W9a1bdv637aXMQAAY4k6BQBoa3M3G9m+RdKlkrbYXlDjijwnSVJE3Cjpm5LeJGmfpF9Jek+27ojtKUk/zIb6ZESs9aFXSdJFF0nVDbxD+9Ch3p8DABh91CkAwEZ11RBFxNvXWR+Sru+w7mZJN/ceDQCA7lCnAAAblcxFFQAAAABg2GiIAAAAAJQWDREAAACA0qIhAgAAAFBaNEQAAAAASouGCAAAAEBp0RABAAAAKC0aIgAAAAClRUMEAAAAoLRoiAAAAACUFg0RAAAAgNKiIQIAAABQWjREAAAAAEqLhggAAABAadEQAQAAACgtGiIAAAAApUVDBAAAAKC0HBFFZziB7SVJPy46Rw+2SPpF0SF6MGp5pdHLPGp5pdHLTN7Be1lEvKDoECmiTg3cqOWVRi/zqOWVRi8zeQcvlzq1OY8kA/DjiKgUHaJbtqvkHaxRyzxqeaXRy0zewbNdLTpDwqhTAzRqeaXRyzxqeaXRy0zewcurTvGWOQAAAAClRUMEAAAAoLRSbYhuKjpAj8g7eKOWedTySqOXmbyDN4qZh2XUjg15B2/UMo9aXmn0MpN38HLJnORFFQAAAABgGFI9QwQAAAAAA0dDBAAAAKC0ht4Q2d5ke4/tb7RZ9zzbf2t7n+17bW9vWfen2fIf235jInn/xPbDth+w/X9tv6Rl3bLt+exxx7DydpH53bYXW7L9Ucu6a23/NHtcm0je/9qS9Se2/6llXSHH2PZjth/Mfu4Jl3t0ww3Z6/UB269qWTf0Y9xF3ndmOR+0PWP7gm6fW2DmS20/1fL7/3jLuiuyvxP7bH80kbwfacn6UPbaPaOb5w4o72m2v2r7R7Yfsf3aVeuTeg0PG3Wq8MzUqf7zUqeKz0yd6i/vcOtURAz1IelPJH1Z0jfarPv3km7Mvr5a0t9mX58v6X5Jz5P0UkmPStqUQN7LJD0/+/r9zbzZ988M+9h2mfndkj7XZvkZkvZn/56efX160XlXbffHkm4u+hhLekzSljXWv0nSnZIs6TWS7i3yGHeR9/eaOSRd2czbzXMLzHxph9f3puzvw7mSnpv93Ti/6Lyrtn2zpO8UeYwl/Q9Jf5R9/VxJp61an9RreNiPdf6GUqcGn/ndok71m3e9v6FJ/TfeRV7q1IDzrtq2dHVqqGeIbJ8l6fclfb7DJlepcQAk6auS/pVtZ8u/EhG/jYifSdon6eKi80bEdyPiV9m390g6a9CZ1tPFMe7kjZLujogjEfGkpLslXZF3vtV6zPt2SbcMNlEurpL0xWi4R9Jptl+sgo7xeiJiJssjJfI67sPFkvZFxP6IqEv6ihq/j5QU+jq2/UJJr5f0BUmKiHpE/NOqzUbqNZwn6tTgUaeSMFL/jVOnhq50dWrYb5n7rKT/JOlYh/VnSvq5JEXEUUlPSfqd1uWZhWzZoH1Wa+dt9V41OtWmf2a7avse2/92ANk6+azWz/wH2enFr9o+O1uW9DHO3ubxUknfaVlc1DEOSXfZnrO9o836TseyqGO8Xt5Wq1/HvTw3T9383Nfavt/2nbZfni1L+hjbfr4af5hv7fW5OXqppEVJO914C9DnbZ+yapvUXsPD9FlRpwbts6JODRp1avCoU4Mz9Dq1ub+83bP9byQ9HhFzti8d1s/dqF7y2r5GUkXSZMvil0TEQdvnSvqO7Qcj4tGBBVbXmf+PpFsi4re2r1NjpvMNg8zVSY+viaslfTUilluWDf0YZy7Jfu6LJN1t+0cRsXsIP3ejuspr+zI1Cs0lvT63gMz3qfH7f8b2myR9TdJ5Q8jVSbfH6c2Svh8RRzbw3LxslvQqSX8cEffa/gtJH5X0nwf4M0cCdYo6tRp1amioU4NHnVrDMM8QvU7SW2w/psbpwTfY/l+rtjko6WxJsr1Z0gslPdG6PHNWtqzovLL9ryX9maS3RMRvm8sj4mD2735J/0/ShQPO21XmiHiiJefnJV2UfZ3sMc5crVWnbws6xq0/93FJt+vEt8V0OpZFHONu8sr2K9R4PVwVEU/08twiMkfE0xHxTPb1NyWdZHuLEj7GmbVex8M6xguSFiLi3uz7r6pReFol9RoeIurU4FGnhoA6NXjUqYEafp2KIX8ILdb+oNn1Wvlh1b/Lvn65Vn5Ydb+G9GHVdfJeqMYH485btfx0Sc/Lvt4i6acawgfmusz84pav3yrpnnj2Q2g/y7Kfnn19RtF5s3X/XI0P9LnoYyzpFEkvaPl6RtIVq7b5fa38oN8PijrGXeY9R43PO/xer88tMPNE8/Wgxh/mA9nx3pz9fXipnv2w6suLzpute6GkI5JOSeAYf0/Sy7Kv/4ukT6f6Gi7qscbfUOrU4DNTp/rLSZ1K4xhTp/rLPNQ6NbS3zHVi+5OSqhFxhxofnvqftvep8Qu5WpIiYq/tv5P0sKSjkq6Plaeki8r7aUmnSvrftiXpQES8RdLvSvpvto+pcRbuUxHxcBF522T+D7bfosZxPKLG1XwUEUdsT0n6Yfa0T8bK06VF5ZUar4OvRPZKzxR1jLdJuj37fW+W9OWI+Jbt90lSRNwo6ZtqXP1kn6RfSXpPtq6IY9xN3o+r8RmIv862OxoRlU7PHXDebjO/TdL7bR+V9GtJV2evj6O2PyDp22pcyefmiNibQF6p8T92d0XEL9d77oDzSo0rYX3J9nPVKMzvSfg1XDjq1NAzU6f6Q50aPOrU4A21Tnnlf7sAAAAAUB5DvzErAAAAAKSChggAAABAadEQAQAAACgtGiIAAAAApUVDBAAAAKC0aIgAAAAAlBYNEQAAAIDS+v9CJoEaoMC34AAAAABJRU5ErkJggg=="
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